GAMES Webinar 2021 – 188期(视觉专题) | 三维深度学习如何“卷”:从建模到渲染
【GAMES Webinar 2021-188期】(视觉专题)
活动标题:三维深度学习如何“卷”:从建模到渲染
活动组织:韩晓光、徐泽祥、周晓巍
时间:北京时间 2021年6月28日晚上8:00pm – 9:45pm
详细 日程:
8:00pm – 8:05pm: 主持人介绍活动流程和背景
8:05pm – 8:10pm: 韩晓光博士介绍三维深度学习在建模方向的发展
8:10pm – 8:15pm: 徐泽祥博士介绍三维深度学习在渲染方向的发展
8:15pm – 9:45pm: Panel 讨论(主持人也会相应参与) 每位嘉宾 1分钟自我介绍;就预先收集的问题开始讨论;观众互动问题讨论
嘉宾简介:
嘉宾1:刘洋(微软亚洲研究院)
个人简介:微软亚洲研究院高级研究员。2000 年和 2003 年于中国科学技术大学数学系获得理学学士及硕士学位,2008 年于香港大学计算机系获得博士学位,2008 年至 2010 年于法国 INRIA/LORIA 研究所从事博士后工作,2010 年至今工作于微软亚洲研究院网络图形组。研究兴趣主要在几何建模与处理、数据驱动的几何重建与分析等方向。担任Geometric Modeling and Processing 2019 和 Shape Modeling International 2021 的(共同)程序主席,目前是IEEE TVCG和ACM TOG的副主编。
个人主页:https://www.microsoft.com/en-us/research/people/yangliu/
嘉宾2:董悦(微软亚洲研究院)
个人简介:董悦博士现任微软亚洲研究院网络图形组主管研究员。他的主要研究方向是计算机图形学中的表观建模(Appearance modeling)、神经网络渲染(Neural rendering)及其他基于深度学习的计算机图形学算法研究。 董悦博士毕业于清华大学高等研究院,师从沈向洋教授,钻研计算机图形学中的表观建模问题,并获得博士学位。
个人主页:http://yuedong.shading.me/
嘉宾3:张寅达(Google)
个人简介:I am a Research Scientist at Google. My research interests lie at the intersection of computer vision, computer graphics, and machine learning. Recently, I focus on empowering 3D vision and perception via machine learning, including dense depth estimation, 3D shape analysis, 3D scene understanding, and digital human. I received my Ph.D. in Computer Science from Princeton University, advised by Professor Thomas Funkhouser. Before that, I received a Bachelor degree from Dept. Automation in Tsinghua University, and a Master degree from Dept. ECE in National University of Singapore co-supervised by Prof. Ping Tan and Prof. Shuicheng Yan.
个人主页:www.zhangyinda.com
嘉宾4:周晓巍(浙江大学)
个人简介:周晓巍,浙江大学博士生导师。2008年本科毕业于浙江大学,2013年博士毕业于香港科技大学,2014至2017年在宾夕法尼亚大学 GRASP 机器人实验室从事博士后研究。2017年入选国家级青年项目并加入浙江大学。研究方向主要为计算机视觉及其在混合现实、机器人等领域的应用。相关工作十余次获得计算机视觉三大顶级会议口头报告(<5%)。担任计算机视觉顶级期刊International Journal of Computer Vision编委、顶级会议CVPR21/ICCV21领域主席。现负责运营图形学与混合现实研讨会(GAMES)。
个人主页:xzhou.me
嘉宾5:张修明(MIT CSAIL)
个人简介:Xiuming Zhang is currently a Ph.D. candidate, advised by Prof. William T. Freeman, in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at Massachusetts Institute of Technology (MIT). Xiuming works in the fields of computer vision and computer graphics, with particular interests in relighting, view synthesis, and material modeling. He received an S.M. (CS) from MIT in 2018 and a B.Eng. (EE) with the Lee Kuan Yew Gold Medal from National University of Singapore (NUS) in 2015. At NUS, his bachelor’s thesis was advised by Prof. B. T. Thomas Yeo on Bayesian modeling of brain disorder heterogeneity. Recognized by a Snap Research Fellowship in 2019, his research was placed on exhibition in the MIT Museum and covered by various popular press outlets, including BBC, Forbes, Yahoo!, and Popular Mechanics.
个人主页:http://people.csail.mit.edu/xiuming
嘉宾6:鲜文琦(Cornell Tech)
个人简介:She is a third-year PhD student in Computer Science at Cornell Tech, being advised by Noah Snavely. Her research interests lie at the intersection of Computer Vision, and Graphics, with a focus to democratize the content creation of videos and special effects. She has been a intern at Adobe and Facebook in the past summers.
个人主页:https://www.cs.cornell.edu/~wenqixian/
主持人介绍:
韩晓光博士现为香港中文大学(深圳)助理教授,校长青年学者。其研究方向包括计算机视觉、计算机图形学以及医疗图像处理等,在该方向著名国际期刊和会议发表论文40余篇,包括顶级会议和期刊SIGGRAPH,CVPR,ICCV,ECCV, NeurIPS, ACM TOG, IEEE TPAMI, IEEE TVCG等。他的团队目前包括博士研究生10名和硕士研究生4名。他的团队连续两年获得CVPR最佳论文提名(入选率为0.8%和0.4%),他的团队主推的DeepFashion3D数据集获得Chinagraph开源数据集奖,他的工作曾获得计算机图形学顶级会议Siggraph Asia 2013新兴技术最佳演示奖,入选2016年年度最佳计算论文之一,他的团队于2018年11月获得IEEE ICDM 全球气象挑战赛冠军(参赛队伍1700多)。更多细节详见http://mypage.cuhk.edu.cn/academics/hanxiaoguang/
Zexiang Xu is a Research Scientist at Adobe Research. He obtained his Ph.D. from University of California San Diego in 2020, advised by Prof. Ravi Ramamoorthi. His Ph.D. research was recognized by Adobe Research Fellowship (2019) and UCSD Chancellor’s Dissertation Medal (2021). His areas of research lie at the intersection of computer graphics and computer vision, mainly about high-quality scene reconstruction and rendering. His recent work focuses on developing novel neural approaches to address classical problems of relighting, view synthesis, 3D reconstruction, appearance modeling, and rendering.
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